Dedicated Read Nodes are now in public preview ‼️ If you're doing semantic search or recommendations at billions of vectors, this is built for you Real use's of the product today: ‣ 135M vectors, 600 QPS sustained, ~45ms P50 latency. ‣1.4B vectors (filtered), 5.7k QPS at

Docs:

🚀 Pinecone Assistant now supports GPT-5! Set model to gpt-5 to get started.

Commvault, Pinecone Team on Enterprise AI Stack Resilience

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15 years of one-party rule means working people get screwed while the rich get richer. Steve Hilton has the business and reform experience to go to Sacramento, take on the system, and fix the high costs of housing and energy. Are you with us?

Revisiting our chunking strategies guide  7 methods for breaking down text for RAG systems Fixed-size → semantic → contextual chunking with LLMs

Knowledgeable AI is only as good as the data that powers it, and how well that data is protected. We’re teaming up with @Commvault to bring immutable backups and PITR to vector workloads. It's a new foundation of trust for enterprise AI use cases.

Day 4: Built a RAG AI agent on N8N using pinecone Also, shoutout to @pinecone uper beginner-friendly and surprisingly generous API limits. Made learning vector search way smoother.

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Agentic retrieval > traditional RAG Three key advantages: • Better query understanding through reasoning • Dynamic multi-source retrieval • Iterative generation for higher quality

Choose what fits your use case:

Vector workloads aren't one-size-fits-all, so Pinecone serverless now offers two modes: On-Demand: Pay per request, auto-scale Perfect for RAG, prototypes, bursty workloads DRN: Reserved nodes, predictable cost Built for billion-vector search with sub-100ms latency

See how companies are building with Pinecone:

4 practical use cases for vector databases: • semantic search • hybrid search • recommendations • RAG which one are you shipping first?

https://t.co/0G60xgvxaW

Back to basics to wrap up the year: We wrote a breakdown of what vector databases actually are and how they work. Still one of the best explanations of the infrastructure powering AI memory and semantic search.

Generate hundreds of ad variations that all feel like they came from the same brand system. Faster testing. Cleaner creative control. Scale without sacrificing identity.

What makes pinecone-sparse-english-v0 work? ✓ Whole word tokenization for precise entity matching ✓ No query expansion = minimal latency overhead ✓ No document expansion = fast indexing without hallucination risk ✓ CoCondenser-based LM Encoder for superior contextual

We use this analogy constantly (for good reason: it works). Play with the animation yourself within:

"Think of data as water flowing from a hose..." This is one of our favorite ways to explain compaction within our slab architecture: 🚰 The hose only fills small cups (L0 slabs)  🪣 Too many cups? They're poured into buckets (L1)  🛢️ Buckets pile up? They're poured into barrels

Watch the full talk:

Smart agentic retrieval pattern: while your AI generates recommendations, have it simultaneously create a checklist to verify it met all the criteria. Built-in quality control that catches what LLMs typically miss.

From December 22, Sonic Rumble will never be the same. Face off against Godzilla in a brand-new stage, earn Kaiju-chaotic rewards, and join the ultimate monster mayhem. 🌊 Here’s what’s lurking: - New Stage – Godzilla Survival - New Characters & Buddies - Each Kaiju brings